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The benefits of Hyperion hyperspectral data to agriculture have been studied at sites in the Coleambally Irrigation Area of Australia. Hyperion can provide effective measures of agricultural performance through the use of established spectral indexes if systematic and random noise is managed. The noise management strategy includes recognition of "bad" pixels, reducing the effects of vertical striping, and compensation for atmospheric effects in the data. It also aims to reduce compounding of these effects by image processing. As the noise structure is different for Hyperion's two spectrometers, noise reduction methods are best applied to each separately. Results show that a local destriping algorithm reduces striping noise without introducing unwanted effects in the image. They also show how data smoothing can clean the data and how careful selection of stable Hyperion bands can minimize residual atmospheric effects following atmospheric correction. Comparing hyperspectral indexes derived from Hyperion with the same indexes derived from ground-measured spectra allowed us to assess some of these impacts on the preprocessing options. It has been concluded that preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results. If these or equivalent preprocessing steps are followed, it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes. Red-edge and leaf chlorophyll indexes based on the preprocessed data are shown to distinguish different levels of stress induced by water restrictions.

There are many techniques for measuring leaf area index (LAI) and forest canopy foliage profiles but their accuracy is questionable. This paper briefly reviews current methods of estimating forest LAI and presents a novel, ground-based laser system, Echidna that can make a wide range of measurements of forest structure, including LAI. Here, use of the system to provide field data and derived gap probabilities in the form of a 'hemispherical photograph with range' is demonstrated. The results show consistency and reproducibility and do not depend on special conditions for the natural light field.

Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 ''discrete'' returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna Ò Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (D height \ 2.5 m) and the vertical distribution of foliage and leaf area (0.86 [ r 2 [ 0.90, p \ 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration.

By jointly inverting several different kinds of geophysical measurements at a site we avoid some of the ambiguity inherent in the individual methods. We show how this can be done for the combination of DC resistivity and magnetotelluric measurements on a layered medium by considering a simple 3-layer model. The combination resolves the resistivity of the thin resistive second layer, even though neither of the two methods can do so alone.The method is then applied to field data from a shallow sedimentary basin. A blind zone occurs beneath a thick near-surface conductive shale. By a study of the eigenvalue structure of the model it can be seen that resolution in this zone would be slightly enhanced by higher frequency magnetotelluric data, but additional DC data at larger spacing would yield no improvement.